package clustering_algorithms;

import java.util.ArrayList;
import java.util.HashMap;
import java.util.Random;

public class DummyData {

	final int number_of_enties = 1000000;
	final int dimension = 1;
	Random rand = new Random();
	public ArrayList<HashMap<Integer, Float>> getDummyData(int number_of_clusters)
	{
		rand.setSeed(123456789);
		ArrayList<HashMap<Integer, Float>> return_value = new ArrayList<HashMap<Integer,Float>>();
		
		
		for (int i = 0; i < number_of_enties; ++i)
		{
			
			int chosen_cluster = rand.nextInt(number_of_clusters);
			
			HashMap<Integer, Float> new_vector =  new HashMap<Integer, Float>();
			System.out.println();
			//
			for(int d = 0; d< dimension;++d)
			{
				//if((i %2 == 0))
				
				{
					//Float next_val = (Float)((float)(rand.nextGaussian()) + (i%1)*1f)*100;
					Float next_val = (float)(i*100);
					new_vector.put(d, next_val);
					System.out.print(","+ next_val);		
				}
				
			}
			System.out.print(";");
			return_value.add(new_vector);			
		}
		/*
		for(int i =0; i<3; ++i)
		{
			HashMap<Integer, Float> new_vector =  new HashMap<Integer, Float>();
			//new_vector.put((0, -10f);
			//new_vector.put((1, -10f);
			//return_value.add(new_vector);
		}
		*/
				
		return return_value;
	}
	
	
	
	 
	
	
}
